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to Watch in 20188 Analytics Trends

for the Intelligent Enterprise

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The Future Belongs to the Intelligent Enterprise

With the world’s biggest brands being constantly

challenged and upended by disruption, a recent survey

reveals that 85% of enterprise decision makers feel they have

just two years to make significant inroads on digital transformation before

suffering financially and/or falling behind their competitors.

Ready or not, the future is here. For enterprise organizations, it must

be a data-driven one.

Whoever can use technology to transform the customer experience, and be the

first to discover and deliver on new business models, will be the disruptor. Those

who can’t, the disrupted in this period known as the “era of Digital Darwinism.”

The future belongs to the Intelligent Enterprise which anticipates constantly

evolving regulatory, technological, market, and competitive challenges and turns

them into opportunity and profit. It connects to any data and distributes reports

to thousands. The Intelligent Enterprise goes beyond business intelligence,

delivering transformative insight to every department, device, and constituent.

As we hone and focus our organizations’ 2020 (and even 2030) vision,

MicroStrategy has compiled top trends enterprise organizations should be

watching and acting on from leading influencers in business intelligence, data

analytics, and digital transformation, including Boris Evelson, Michele Goetz,

David Menninger, Jen Underwood, Ronald van

Loon and Ray Wang.

From the Internet of Things and artificial intelligence, to

machine learning and natural language generation,

to some very human factors, we hope you’ll

find this gathering of insights a resource for

looking and planning ahead.

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TREND 1: AI WILL RESHAPE ANALYTIC AND

BUSINESS INNOVATION

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From the Forrester blogs: “If CIOs and chief data officers (CDOs) are serious about

becoming insights driven, 2018 is the year they must realize that simplistic lift-and-shift

approaches will only scratch the surface of possibilities that new tech offers.

In Forrester’s 2018 AI predictions, we provide CIOs working on data and analytic

initiatives with pragmatic and tangible recommendations on how to act now with their

AI initiatives. Here are just a few ways we predict AI will shape enterprises in 2018:

• AI will reshape analytic and business innovation: A quarter of firms will

supplement point-and-click analytics with conversational user interfaces, and AI will

make decisions and provide real-time instructions at 20% of firms.

• Big Data environments will evolve or suffer the same fate as yesterday’s data

management: One-third of enterprises will take their data lakes off life support in

2018, and half of firms will adopt a cloud-first strategy for big data analytics.

• Firms will remake traditional data and analytic roles to activate insights: Two-

thirds of firms will create customer insight centers of excellence, and data engineers

will become the new hot job title in 2018.

• The insights market landscape will become as complex as three-dimensional

chess: The IaaS (Insights-as-a-Service) market will double, with 80% of firms relying on

insights service providers for some portion of insights capabilities in 2018.

2018 will be the year that CIOs will realize that new technologies like AI require hard

work. Forward-looking organizations will create new roles and processes to take

full advantage of them – not by simply shifting away from old architectures, but by

redesigning their whole operating models to suit the new wave of technology.”

TREND 1: AI WILL RESHAPE ANALYTIC AND BUSINESS INNOVATION

Boris EvelsonVP & Principal Analyst, Forrester Research

Boris Evelson is a leading expert in

business intelligence (BI) and delivers

strategic guidance, helping enterprises

define BI strategies, governance, and

architectures and identify vendors

and technologies that help them put

information to use in business processes

and end user experiences. He has

more than 30 years of experience with

enterprise software and applications

implementation, management

consulting, and strategic advisory skills

which includes executive roles at Textra,

JPMorgan, PricewaterhouseCoopers

and Citibank.

Michele GoetzPrincipal Analyst, Forrester Research

Michele Goetz serves enterprise

architects, chief data officers, and

business analysts trying to navigate

the complexities of data while

running an insight-driven business.

Her research covers artificial

intelligence technologies and

consultancies, semantic technology,

data management strategy, data

governance, and data integration.

Michele has more than 20 years of

experience in data management,

business intelligence, and analytics.

Prior to joining Forrester, she was the

VP of Marketing at Trillium Software.

“2018 will be the year that CIOs will realize that new technologies like AI require hard work.”

Boris Evelson, Michele Goetz, Predictions 2018: AI Hard Fact –

Treat It Like a Plug-And-Play Panacea and Fail, Forrester Research Blogs, November 9, 2017

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TREND 2: COMPETITION FOR DATA SCIENCE

AND ANALYTICS TALENT

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TREND 2: COMPETITION FOR DATA SCIENCE AND ANALYTICS TALENT

“In 2018 and beyond, enterprise organizations need to focus attention not just on their

recruiting efforts for top analytics talent, but also retention efforts, as the shortage for

those with data science and analytics skills begins to grow.

A Business Higher Education Forum (BHEF) and PwC report titled Investing in America’s

Data Science and Analytics Talent: The Case for Action predicts that in 2020, there will be

2.7 million job postings for data science and analytics roles. Many of these will not be

traditional engineering, operations or IT roles. These will be spread across the entire

organization. In a related BHEF and Gallup survey, 59% of employers said data science

and analytics skills would be required of all finance and accounting managers by 2020;

51% said these skills would be required by all marketing and sales managers; 49% said

they would be required of all executive leaders; and 48% said they would be required of

all operations managers.

The challenge lies in that less than five percent of college students today are taking

courses in data science and analytics, and the future supply of talent isn’t predicted

to grow to meet needs. While 69% of employers focused on data’s role in digital

transformation say in the next few years they will prefer job candidates with data science

and analytics skills over those without, educators say only 23% of all graduates in 2021

are on course to hold these skills.

For enterprise organizations to have the talent they want and need tomorrow, they not

only need to up the ante on their acquisition efforts today, but also prepare to grow

their own talent in house with training and education.”

Teresa GreenVP, Global Talent Acquisition, MicroStrategy

In 2020, there will be 2.7 million job postings for data science and analytics roles.

America’s Data Science and Analytics Talent: The Case for Action

Teresa Green is the VP of Talent

Acquisition for MicroStrategy. Teresa

leads a global team of over 40 talent

acquisition leaders focused on today’s

and tomorrow’s top talent for data

science and analytics. Prior to her role

at MicroStrategy Teresa served as the

Head of Global Talent Acquisition at

CEB, now Gartner. She has also held

similar roles at Capital One, Deloitte

Consulting and Marriott International.

Teresa holds a Bachelor of Science in

Business Management from University

of Maryland, University College.

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TREND 3: CONVERGENCE OF REAL-TIME AND

BATCH-BASED ANALYTICS

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TREND 3: CONVERGENCE OF REAL-TIME AND BATCH-BASED ANALYTICS

“The real-time and batch-based analytics worlds have been disconnected. People

with real time analytics have the need to compare it to historical data.

Real-time isn’t (just) what’s in the stream, but includes streaming and processing

the historical data, as well. Let’s imagine a retail store using sensors to measure and

monitor traffic in real-time. With historical data, the analytics application can predict

the most profitable product categories, and by combing the real-time distribution

of customer traffic, sales personnel and historical sales, it can recommend the

optimal distribution of sales people.

More organizations use and store data from multiple sources (structured and

unstructured), and they need to be able to act on it in ‘real-time.’ The fusion of real

time data and analytics from historical data enables a host of new use cases for

actionable analytics.”

Tim LangChief Technology Officer, MicroStrategy

80% of retailers worldwide say they agree that the Internet of Things will drastically change the way companies do business in the next three years. More than 70% of surveyed retailers have sensor-related projects underway.

Retail Systems Research: The Internet of Things in Retail: Great Expectations

Tim Lang is the CTO of MicroStrategy.

Previously, he served as the Chief

Product Officer and Senior Vice

President of Talemetry Inc., as well

as its Vice President of Product

Strategy. During his career, he also

served as the Vice President of

Product Management (Reporting

and Visualization product lines) at

SAP AG, where he was responsible

for software engineering, software

testing and product management.

He was responsible for Product

Management across the Business

Intelligence business (SAP Business

Objects). Tim holds a degree in

Information Management from The

University of Melbourne (Australia).

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TREND 4: VOICE AND NATURAL LANGUAGE INTERFACES

BECOME MAINSTREAM

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TREND 4: VOICE AND NATURAL LANGUAGE

INTERFACES BECOME MAINSTREAM

“Voice and natural language processing will be the most significant enhancement to

man machine interfaces since the advent of the graphical user interface.

For decades, the man-machine interface has been based on keyboard and mouse

interactions. We have learned to use these devices, but neither is natural to us, and as a

result, it restricts the audience for various technology initiatives including analytics and

business intelligence. Language is our normal means of communication for everyday

interactions. As our devices of all types allow users to leverage language, we will see

adoption of related types of technology among a broader audience. This is particularly

relevant for analytics initiatives which have traditionally struggled to reach widespread

and regularly usage throughout an organization.

Voice is not the only trend to watch. Natural language generation has already started

to make its mark, as well. Not everyone knows how to read a table of numbers or a

chart, or agrees on what conclusions to draw from the same. A written summary can

be consumed by all and reduces the ambiguity of many types of data displays.

As an industry, we have been obsessed with providing maps and geographical displays

as part of our information systems, but our research shows that users consider text more

important than maps. Voice and natural language interfaces are becoming a reality

today. Organizations should be exploring these options and finding ways to incorporate

them into their systems to maximize the value of their technology investments.”

David MenningerSVP & Research Director, Ventana Research

By 2020, 50% of all searches will be voice searches.

comScore forecast

David Menninger is responsible

for the overall research

direction of data, information

and analytics technologies at

Ventana Research covering

major areas including analytics,

big data, business intelligence

and information management

along with the additional specific

research categories including IT

performance management and IoT.

David has more than 25 years of

experience bringing leading-edge

data and analytics technologies

to market. He served as the Head

of Business Development at

Strategy at Pivotal (Dell/EMC);

and VP of Marketing and Product

Management at Vertica Systems,

Oracle, Applix, InforSense and

IRI Software. David has an MS in

Business from Bentley University

and a BS in Economics from

University of Pennsylvania.

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TREND 5: CONVERGENCE

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“The analytics market will continue to see convergence across three vectors: vendor

convergence (in the market), emerging technology convergence, and tool convergence

(within organizations):

Vendor Convergence: The merger and acquisition activity will continue in the broader

analytics space. The analytics market continues to be one where new players can add

value mapped to niche opportunities. These solutions solve a pain point that results in a

number of quarters of revenue and growth, yet long-term growth isn’t typically feasible,

so each one of these companies then looks to make true on their original exit strategy.

These exits usually involve either quickly selling to a larger player to make money for

the investors, or recoup some money before the lack of profitability forces the company

out of business. This M&A activity will continue to reduce the number of vendors in

the space, yet it’s such a vibrant space that new vendors will emerge to replace them,

innovating at specific tasks for a specific subset of organizations.

The larger vendors either acquire these smaller companies (and their customers) or

innovate to create their own approaches to solving the problems. Many new smaller

companies have emerged in the last five years. Yet many struggle to financially make

ends meet, and 2018 will see a lot more M&A activity in the analytics market.

Recommendation: if you want a quick solution that needs to work in one to two years,

pick whatever tool you want. If you want something for three years or more, pick a

vendor that isn’t courting a big stack tech behemoth or in need of saving from a private

equity financier.

Emerging Technology Convergence: More than in previous years, the range of

technological advances (or waves of tech evolution) are greater in variety and multitude

than ever before. Beyond mobile, cloud, big data, data discovery, and security are voice,

AR, VR, AI, next-gen analytics, IoT, telemetry, and more. These emerging technologies are

mature enough to provide various and immediate business opportunities, based on a

foundation of business data.

In 2018, these emerging technologies will continue to overlap and merge. Next-gen

analytics will integrate with AI for augmented intelligence. Machine learning will only

reap its full value on big data (that’s structured or semi-structured). Voice (and smart

bots) will require AI to truly bring transformative experiences to users who want to ask

questions of information.

TREND 5: CONVERGENCE

Hugh OwenSVP, Product Marketing, MicroStrategy

Hugh Owen is the Senior Vice President

in charge of Product Marketing for

MicroStrategy. He is responsible for the

product marketing of MicroStrategy’s

Enterprise Analytics and Mobile

Software platforms. Prior to running

Product Marketing, he launched and ran

MicroStrategy’s Mobile Product business

(launch in 2010). From 2006-2010 he

worked as a Senior Product Manager,

during which he launched multiple new

MicroStrategy Business Intelligence

products. From 2000-2006 he cut his

BI teeth in MicroStrategy Technology

Services as a Technical Support Engineer

and Premium Support Engineer. Hugh

holds a Bsc (Hons) degree in Business

from The University of Bath, England.

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TREND 5: CONVERGENCE (cont.)

“In 2018, we’ll continue to see organizations standardizing on a platform on which they based their analytics ecosystem, and merging as many projects into that solution as possible, and connecting various clients and enterprise assets and third-party libraries to it where necessary.”

Hugh Owen

Telemetry data from security systems will generate so much information that only

big data storage will be able to capture the machine-like data from people – and

only next-gen analytics will be able to take advantage of the telemetry data to

provide answers and analytics based on proximity. And next-generation analytics

will combine with AI and cloud computing for smart-scaling – tapping into cloud

computing which is available like water. The intersections will be various, but the

evolution of each technology will increasingly rely on other technologies around it to

deliver real value.

Tool Convergence: Organizations, specific to their analytics needs, have compiled

collections of products to solve various problems. These different problems span

different departments (with different tools), include different tools integrated

together to solve specific problems within departments, and even have different

tools fighting to solve the same problems.

The consumerization of IT empowered organizations (and departments and users)

to access to a large array of products to help them fix (or bandage) their problems

immediately. Rather than waiting for IT/IS to provide a solution, users downloaded

free product, paid using credit cards for tools from the web, used open source

technology, and did what business people often do – make do now to make

themselves look good in front of their boss in their next review.

IS/IT are now finally being empowered by two trends – powerful enterprise software

that has caught up or leapfrogged the free or cheap tools from the web… and

various failed projects that can’t scale past the excitement of projects that look good

yet can’t scale. Pretty attractive pictures don’t run businesses. Yet when powerful

enterprise tools are suddenly great looking too, IS/IT and business both have what

they want. This results in less or little need for the various tools companies have

acquired over the past five-plus years.

In 2018, we’ll continue to see organizations standardizing on a platform on which

they based their analytics ecosystem, and merging as many projects into that

solution as possible, and connecting various clients and enterprise assets and third-

party libraries to it where necessary. It’ll act more as the beating heart to the analytics

ecosystem than the tool of choice… users will get powerful Mac, PC, web and mobile

clients to connect to their analytics; managers and executives will see the business

running on data they can rely on, and IS will get fewer tools to manage and pay for,

the governance they need, and happy business users working alongside them to

digitally transform their respective businesses.”

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TREND 6: EMERGENCE OF

AUGMENTED ANALYTICS

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TREND 6: EMERGENCE OF AUGMENTED ANALYTICS

“We are witnessing an unprecedented pace of continuous technological change. New

augmented analytics approaches will alter the analytics landscape once again. Search,

natural language and intelligent analytics automation innovations powered by AI are

beginning to vastly transform the human-computer experience democratizing the

power of analytics and data science.

Similar to the second wave of modern self-service BI disrupting the first wave of traditional

BI, augmented analytics technologies in the third wave will change the game once again.

Augmented analytics combines the beauty of the human mind and artificial intelligence.

Augmented analytics approaches are smart, forward thinking and actionable. In addition

to providing historical reports and dashboards, augmented analytics automates predictive

and prescriptive actionable guidance. Early adopters of augmented analytics tout

unmatched speed to insight and enhanced competitive advantage.

Automating analytics is not a novel idea. This old concept is rapidly improving and

expanding across the entire analytics life-cycle from finding data sources to discovering

crucial insights. Next generation augmented analytics capabilities can automatically prepare

and cleanse data, perform feature engineering, identify and rank key insights, answer what-if

questions, provide suggestions and reveal hidden patterns in oceans of data. Automation

expedites investigation across millions of variable combinations that would be far too time

consuming for a human to do manually.

When given unbiased, properly prepared data, augmented analytics delivers amazing

results. Since automated analytics relies on statistical techniques, inaccurate, biased

or poor-quality data that doesn’t sufficiently represent business processes will deliver

low quality results. Think garbage in, garbage out. Much like preparing your data for

statistics, data warehousing, or operational reporting applications, there is an art to

preparing your data for automated augmented analysis.”

Jen UnderwoodFounder, Impact Analytix

“Similar to the second wave of modern self-service BI disrupting the first wave of traditional BI, augmented analytics technologies in the third wave will change the game once again.”

Jen Underwood

Jen Underwood is the founder of

Impact Analytix, LLC and a recognized

analytics industry expert. Her

experience includes a unique blend

of worldwide analytics product

management, design, and more than

20 years of “hands-on” data warehouse,

reporting, visualization and predictive

analytics solution development.

She writes for InformationWeek,

O’Reilly Media and other industry

publications. Jen holds a Bachelor of

Business Administration – Marketing,

Cum Laude from the University of

Wisconsin, Milwaukee and a post-

graduate certificate in Computer

Science – Data Mining from the

University of California, San Diego.

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TREND 7: MACHINE LEARNING, AI AND EDGE AND

VIDEO ANALYTICS

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TREND 7: MACHINE LEARNING, AI AND EDGE AND VIDEO ANALYTICS

“Digital technologies are disrupting and forever altering the enterprise landscape,

and there are upcoming trends that indicate truly compelling applications for

organizations of all industries.

A key trend is arising around artificial intelligence and machine learning capabilities,

which allows enterprises to build a real-time data and analytics infrastructure that

drives valuable analytics-based insights. All large software vendors are currently

adding AI and ML capabilities into their existing software applications.

This indicates a movement towards mainstream AI and ML applications within the

next year, as these companies start augmenting human capabilities and increasing

efficiency levels with the support of these digital capabilities. Ease of use, in particular,

is propelling the transition from ‘buzz’ to mainstream. Cost-effectiveness, speed,

responsiveness, and intelligent automation are crucial components that factor into its

pervasive adoption by large companies.

Artificial intelligence and machine learning will continue to be a valuable analytics-

driven trend for years to come as companies reshape their infrastructures and

models to cater to individual consumer needs, improve productivity, and automate

repetitive tasks.

Secondly, there’s rapid growth in edge analytics stemming from the massive increase

in connected devices, which is forecasted to reach 30 billion by 2020. Many devices

are generating vast amounts of data that cannot be centrally analyzed, and edge

analytics is providing the solution. Its applications will continue to increase in the

coming years, corresponding to the increase in IoT enabled sensors that gives

organizations the ability to perform real-time analyses at any point where data is

generated, whether it’s a network switch, sensor, or connected device.

Edge analytics will help companies address challenges related to centrally analyzed

data generated from so many connected devices. The huge volumes of streaming

data can pose data management difficulties for businesses including delayed analysis

as a result of overtaxed central systems and slow or restricted network availability.

A notable third trend is video analytics. As video camera use continues to increase

from connected devices like smart phones, tablets, and security cameras, videos are

becoming another source of valuable data generation for businesses. Accordingly,

video analytics is becoming a rising trend fueled by digital capabilities like AI and IoT.

Ronald van LoonTop 10 Analytics Influencer

Ronald van Loon is globally

acknowledged as a Top 10 Influencer

in big data, IoT, data science, machine

learning and predictive analytics.

He is ranked among Onalytica’s Top

Global Big Data Influencers. Ronald

helps organizations in their digital

transformation efforts, including

providing insights and best practices

to become more data-driven and

customer-centric. Recently appointed

as Advisory Board Member for

Simplilearn, he is a frequent public

speaker at industry events and is an

author for Dataconomy, Datafloq and

Data Science Central.

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TREND 7: MACHINE LEARNING, AI AND EDGE AND VIDEO ANALYTICS

“Around 29 billion connected devices are forecast by 2022, of which around 18 billion will be related to IoT.”

Ericsson Mobility Report,

June 2017

Videos are now another medium for obtaining information, and companies

are utilizing video analytics in retail, law enforcement and city surveillance.

When augmented with intelligent technologies like AI and machine learning,

organizations are able to utilize applications for video analytics that include facial

tagging, tracking technologies, and micro-expression analysis.

Powered by AI, video analytics can quickly analyze enormous volumes of

streaming data contained within video to identify objects like people, cars,

trucks, traffic, and license plates in real-time. Video analytics also facilitates faster

communication across systems, enabling organizations to make swift decisions,

predictions, and be proactive in developing intelligent solutions.

As cities increase their development of smart cities, smart transportation,

and smart buildings, video analytics is becoming an increasingly viable and

valuable solution.”

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TREND 8: ACCESS VS. OWNERSHIP OF ANALYTICS AND

INSIGHT STREAMS

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TREND 8: ACCESS VS. OWNERSHIP OF ANALYTICS AND INSIGHT STREAMS

R. “Ray” WangFounder & Principal Analyst, Constellation Research

“Data is the foundation of Digital Business. Every touch point, every click, every byte of digital exhaust.”

R. “Ray” Wang

R “Ray” Wang the Principal Analyst,

Founder, and Chairman of Silicon

Valley based Constellation Research,

Inc. He’s also the author of the

popular business strategy and

technology blog “A Software Insider’s

Point of View.” Wang has held

executive roles in product, marketing,

strategy, and consulting at companies

such as Forrester Research, Oracle,

PeopleSoft, Deloitte, Ernst & Young,

and Johns Hopkins Hospital. Ray

is a regular contributor to Harvard

Business Review and well quoted

in The Wall Street Journal, Forbes,

Bloomberg, CNBC TV, Reuters, IDG

News Service, and other global media

outlets. His is also the author of the

new best-selling book Disrupting

Digital Business, published by Harvard

Business Review Press.

“Sixty percent (60%) of mission critical data is no longer within your four walls or data

warehouse/data lake. Organizations should learn how to use “dirty” data to deliver

insights in batch, near-real-time, real-time and predictive streams moving forward.

In addition, one of the biggest opportunities for monetizing analytics will come from

the development of insight streams. These insights will come from least likely sources, as

well the most obvious. Least likely sources include power consumed, water used, visitors

into a building, foot traffic on the sidewalk, and density of parking lots. These sources

may seem mundane or perhaps useless, but large insight brokers will begin taking this

data to drive contextually relevant information. Obvious sources? These include internal

systems such as workforce performance data, customer satisfaction data and product

quality stats. The goal here will be to use this information to differentiate.

Organizations should start focusing on getting their data house in order. Build a

foundation to support strong governance, data prep, streaming and agility.

There are three models to build big data/insight business models that I shared in the

Harvard Business Review and that I’m happy to share in the accompanying graphic (click

here). These should prove helpful to any organization.

Another trend I’d encourage enterprise organizations to watch is infinite ambient

orchestration. Mass personalization is delivered through insights in the background

across networks and platforms. AI-driven smart systems will depend on infinite

ambient orchestration. These systems will augment human decisions. They will predict

outcomes. They will deliver on regulatory compliance, and they will reduce recalls. Read

more here.

Organizations need to build a foundation from data, context, journey design, and

recommendation engines.”

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Get More 2018 Trends and Insights

For more enterprise analytics insights throughout the year, follow these experts on Twitter:

Boris Evelson@bevelson

Michele Goetz@mgoetz_forr

Teresa Green@teresarecruits

Tim Lang@timlangCTO

David Menninger@dmenningervr

Hugh Owen@hughtowen

Jen Underwood@idigdata

Ronald Van Loon@ronald_vanloon

Ray Wang@rwang0

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